King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
University of Tartu, Tartu, Estonia.
Int J Cardiol. 2019 Aug 1;288:140-147. doi: 10.1016/j.ijcard.2019.01.046. Epub 2019 Jan 19.
The In-hospital length of stay (LOS) is expected to increase as cardiovascular diseases complexity increases and the population ages. This will affect healthcare systems especially with the current situation of decreased bed capacity and increasing costs. Therefore, accurately predicting LOS would have a positive impact on healthcare metrics. The aim of this study is to develop a machine learning-based model approach for predicting in-hospital LOS for cardiac patients.
Using electronic medical records, we retrospectively extracted all records of patients' visits that were admitted under adult cardiology service. Admission diagnosis and primary treating physician were reviewed to verify selection criteria. A predictive machine learning-based model approach was applied to incorporate simple baseline health data at admission time to predict LOS. Patients were divided into three groups based on their LOS: short (<3 days), intermediate (3-5 days) and long (>5 days). Information gain algorithm was utilized to select the most relevant attributes. Only attributes with information gain of more than zero were used in model building. Four different machine learning techniques were evaluated and their diagnostic accuracy measures were compared.
The dataset of this study included adult patients who were admitted between 2008 and 2016 in King Abdulaziz Cardiac Center (KACC). The center is located in King Abdulaziz Medical City Complex in Riyadh, the capital of Saudi Arabia.
PARTICIPANTS (DATASET): A total of 16,414 consecutive inpatient visits for 12,769 unique patients (mean age of 58.8 ± 16 years of which 68.2% were males) between 2008 and 2016 were included. The study cohort had a high prevalence of cardiovascular risk factors (hypertension 56%, diabetes 56%, dyslipidemia 52%, obesity 33% and smoking 24%). The most common admitting diagnosis was acute coronary syndrome (36%).
The variables with highest impact on the prediction of in-hospital LOS were on admission heart rate, on admission systolic and diastolic blood pressure, age and insurance status (eligibility). Using machine learning models; Random Forest (RF) model outperformed among all other models (sensitivity (0.80), accuracy (0.80), and AUROC (0.94)).
We showed that machine learning methods provide accurate prediction of LOS for cardiac patients. This is can be used in clinical bed management and resources allocation.
随着心血管疾病复杂性的增加和人口老龄化，住院时间（LOS）预计会增加。这将影响医疗保健系统，尤其是在目前床位容量减少和成本增加的情况下。因此，准确预测 LOS 将对医疗保健指标产生积极影响。本研究的目的是开发一种基于机器学习的方法来预测心脏病人的住院 LOS。
使用电子病历，我们回顾性地提取了所有在成人心脏病学服务下住院的患者就诊记录。对入院诊断和主要治疗医生进行了审查，以验证选择标准。应用基于预测的机器学习方法，将入院时的简单基线健康数据纳入模型，以预测 LOS。根据 LOS 将患者分为三组：短（<3 天）、中（3-5 天）和长（>5 天）。利用信息增益算法选择最相关的属性。仅使用信息增益大于零的属性构建模型。评估了四种不同的机器学习技术，并比较了它们的诊断准确性度量。
本研究的数据集中包括 2008 年至 2016 年期间在阿卜杜勒阿齐兹国王心脏中心（KACC）住院的成年患者。该中心位于沙特阿拉伯首都利雅得的阿卜杜勒阿齐兹医学城综合大楼内。
参与者（数据集）：共纳入 16414 例连续住院的 12769 例患者（平均年龄 58.8±16 岁，其中 68.2%为男性），时间为 2008 年至 2016 年。研究队列具有较高的心血管危险因素患病率（高血压 56%、糖尿病 56%、血脂异常 52%、肥胖 33%和吸烟 24%）。最常见的入院诊断是急性冠状动脉综合征（36%）。
对住院 LOS 预测影响最大的变量是入院时的心率、入院时的收缩压和舒张压、年龄和保险状况（资格）。使用机器学习模型；随机森林（RF）模型在所有其他模型中表现最佳（敏感性为 0.80、准确性为 0.80、AUROC 为 0.94）。
我们表明，机器学习方法可以准确预测心脏病人的 LOS。这可以用于临床床位管理和资源分配。
